Design of IMC for Nonlinear Systems by Using Adaptive Neuro-Fuzzy Inference System

뉴로 퍼지 시스템을 이용한 비선형 시스템의 IMC 제어기 설계

  • Published : 2001.11.01

Abstract

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

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References

  1. Astrom, K. and Hagglund, T., 'PID controllers: Theory, design and tuning,' 2nd Ed. Instrument Society of America, Research Triangle Park, North Carolina, 1996
  2. Morari M., Zafiriou E., 'Robust process control,' Prentice-Hall International Editions, 1989
  3. Rivera, D. E., M. Morari, and Skogestad, 'Internal model control 4. PID controller design,' Ind. Eng. Chem. Process Des. Dev., vol. 25, p. 252, 1986
  4. J. -S. R. Jang, 'ANFIS: Adaptive-network-based fuzzy inference systems,' IEEE Trans. on Systems, Man, and Cybernetics, vol.23, pp. 665-685, May, 1993 https://doi.org/10.1109/21.256541